Inferring gene regulatory networks from time-ordered gene expression data using differential equations

Abstract

Spurred by advances in cDNA microarray technology, gene expression data are increasingly becoming available. In time-ordered data, the expression levels are measured at several points in time following some experimental manipulation. A gene regulatory network can be inferred by fitting a linear system of differential equations to the gene expression data. As biologically the gene regulatory network is known to be sparse, we expect most coefficients in such a linear system of differential equations to be zero. In previously proposed methods to infer such a linear system, ad hoc assumptions were made to limit the number of nonzero coefficients in the system. Instead, we propose to infer the degree of sparseness of the gene regulatory network from the data, where we determine which coefficients are nonzero by using Akaike’s Information Criterion.